This entry presents the state of the art ML models applied in solar energy’s forecasting field i.e., for solar irradiance and power production forecasting (both point and interval or probabilistic forecasting), electricity price forecasting and energy demand forecasting. Other applications of ML into the photovoltaic (PV) field taken into account are the modelling of PV modules, PV design parameter extraction, tracking the maximum power point (MPP), PV systems efficiency optimization, PV/Thermal (PV/T) and Concentrating PV (CPV) system design parameters’ optimization and efficiency improvement, anomaly detection and energy management of PV’s storage systems. While many review papers already exist in this regard, they are usually focused only on one specific topic, while in this paper are gathered all the most relevant applications of ML for solar systems in many different fields. It gives an overview of the most recent and promising applications of machine learning used in the field of photovoltaic systems.
Today, ML is ubiquitous. When we interact with banks, shop online or use social media, ML algorithms are used to make our experience efficient, easy and safe, along with learning our lifestyle-related preferences. For example, search engines on the Internet practically exploit them in many ways: the results we obtain derive from algorithms that elaborate models and patterns of use of search keys, as well as for completion suggestions. Amazon Go, the first store with no cashiers opened by Amazon in Seattle, is also based on ML and other advanced technologies. Self-driving cars, which we will soon see on the roads, use continuously improved ML models: MIT in Boston has developed a system that will allow these cars to orient themselves only with sensors and GPS, avoiding the use of maps which may simply be out of date or insufficiently detailed. ML is fundamental for data protection and fraud prevention, thanks to unsupervised algorithms that compare the access models and detect any anomalies, and it can also improve personal security, making checks at airports and places of transport more reliable and faster. Applications in the health sector will also be increasingly relevant, to obtain more accurate diagnoses, analyze the risk factors of certain diseases and prevent epidemics . ML and associated technologies are developing rapidly, and we are just starting to discover their capabilities . AI technologies have now also arrived in the field of renewable energy; from those, such as Google, who use them in wind farms to improve forecast data , to those who use them to increase the efficiency of solar panels .
The remainder of this paper is structured as follows: Chapter 2 reports a reasoned introduction about ML methods or more generally data-driven methods, Chapter 3 gathers all more recent review papers on the topics treated in this paper, Chapter 4 is devoted to the field of PV power forecasting, Chapter 5 reports recent papers concerning the anomaly detection (fault diagnostic) in PV, Chapter 6 regards ML-based methods for MPPT in PV, Chapter 7 gives an overview on the other applications of ML in PV field and finally Chapter 8 ends the paper with concluding remarks and an analysis of possible future trends.